CN103150352B - System to generate related search queries - Google Patents
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- CN103150352B CN103150352B CN201310053002.2A CN201310053002A CN103150352B CN 103150352 B CN103150352 B CN 103150352B CN 201310053002 A CN201310053002 A CN 201310053002A CN 103150352 B CN103150352 B CN 103150352B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/242—Query formulation
- G06F16/2425—Iterative querying; Query formulation based on the results of a preceding query
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
- G06F16/9032—Query formulation
- G06F16/90324—Query formulation using system suggestions
Abstract
System and methods are described to provide new recommendations to a search interface to assist users in navigating toward new searches that are likely to generate results aligned with the user's searching intentions. An algorithm analyzes previous search sessions to determine additional locations for the user to go. In an example of a commerce system, new information placement may be added to the top of search and listings pages to show links to new searches that can be run by the user. On a periodic basis, the search recommendations may be updated, for example based on the most current changes in user behavior.
Description
The application is entitled " system for generating related search queries ", the Shen submitted on June 20th, 2006
Please number for 200680022268.9 (PCT/US2006/023807) patent application divisional application.
Technical field
This invention relates generally to data access and search field.
Background technology
The search such as specified by user based on the such networking business system of the auction system of internet is positioning business
Product.Although certain user is good at positioning desired project very much, most of user lacks to be contributed to operating this system
Knowledge.As a result poorly efficient use and the shortage of the customer flow to some commodity of system can be experienced.The poorly efficient use of system will
System operator is asked to use than actually required more computing resources and other resources.
In order to increase sales volume, some business systems provide a user with recommendation based on the purchase situation of previous user.For example,
When product is checked, system can indicate that the user for once buying the product also have purchased the second mark product.The system is not
The efficiency of search system is improved, but is provided solely for suggestion to increase sales volume.
The content of the invention
A kind of method includes：Current queries are received from active user；Using one or more processors calculate once perform with
The current queries identical first is inquired about, performs the second inquiry and in response to described after first inquiry is performed
One or more Search Results that second inquiry is presented perform after at least one search the of the number of the previous user of activity
One counts；And count more than first threshold in response to described first, the described second inquiry is automatically designated as into described first and is looked into
The recommendation query of inquiry, second inquiry will be presented to the active user.
Description of the drawings
Fig. 1 is the page view (page view) of user interface；
Fig. 2 is to illustrate a kind of flow chart of the method for embodiment；
Fig. 3-5 is other page views of user interface；
Fig. 6 is to illustrate a kind of flow chart of the method for embodiment；
Fig. 7-13 is other page views of user interface；
Figure 14 is the block diagram for illustrating network computer system；
Figure 15 is examples shown wide area information server figure；
Figure 16 illustrates based on passing user mutual to generate the example logic of recommendation query；
Figure 17 shows the diagram of the machine presented with the exemplary forms of computer system.
Specific embodiment
Method and system for searching for or accessing data source is described.In the following description, illustrate to illustrate
Many details, providing thorough understanding of the present invention.It will be obvious, however, to one skilled in the art, that
The present invention can be realized in the case of without these details.
In a kind of example embodiment, new recommendation is provided to search interface, to help user to carry out new search, this
A little new search are likely to produce the result matched with the search intention of user.In one embodiment, algorithm is to previous
Search sessions are analyzed, to determine " secondary best placement " that user can go.In an example of business system, new information cloth
Office can be added into the top of search and original list, to illustrate the link towards user's new search to be carried out.Can be such as
Newest period of change ground more new search based on user behavior is recommended.For example see the page view 100 of Fig. 1, which illustrates net
The example information of user is presented on page.Wherein, the search term " Ferrari " in " all categories (all classification) "
Recommend there is provided following relevant search：" Lamborghini ", " Porsche ", " Bentley ", " Aston Martin " and
“Maserati”。
A kind of embodiment attempt by identify user and directing the user to have proved to be successfully search in the past come
Improve search experience.Those users for performing one of search recommendation may more successfully search for and position data interested
.For the unprofessional user of data resource, particularly those needs are with regard to which search term (search term) should be used
For come the user of the more guidances of data item being successfully found on data resource, the additional searching option recommended may be demonstrate,proved
Bright is useful.Such that it is able to computing resource is saved by reducing poorly efficient user's search strategy and total system effect is improved
Rate.
Being confirmed as the search of low-quality user's offer can include offensive term, incoherent term and with inclined
The term of user is driven to mode (for example, be partial to a concrete brand and the competition brand under non-commercial context).As such, it is possible to carry
For the replacement search recommended.
In a kind of example embodiment, search and recommended engine attempt to be searched based on this of observed (or record)
The user of rope and recommended engine recommends (such as query term) for the behavior of information resources to search further for providing.Example
Such as, search and recommended engine can be recommended to include term after the first search inquiry including term " Toyota " is received
The further search inquiry of " Honda ".In one embodiment, the recommendation inquired about is searched further for based on having been observed that
The user of (or record) a certain threshold number provides after the search inquiry for including term " Toyota " is provided and includes art
The search inquiry of language " Honda ".
In addition, in example embodiment, (and search that whether search and recommended engine recommend further search inquiry
How engine carries out ranking to further search inquiry) it is potentially based on user (post- after the search for information resources
Search user) or system action (or activity).For example, have recorded further search inquiry (for example, in search engine
Including term " Honda ") in the case of the Search Results that previously deliver less than predetermined number, for further search inquiry
(relative to the inquiry of other candidate search) recommendation ranking can be lowered.
After search and recommended engine have recorded some search in the case of the presence (or not existing) of user behavior, should
Information can be used to carry out further search inquiry ranking and/or for determining whether that providing further search looks into
Ask as the search inquiry recommended.For example, in the environment of business system, indicate specific in the passing user behavior of record
One or more product/clothes that the user of threshold number recommends to be identified for particular candidate search after candidate search recommendation
In the case that (or participating in auction or some other transactions) is bought in business, the particular candidate search recommendation can be obtained
Recommend higher recommendation ranking to another candidate search than not causing transaction.In one example, search and recommendation is drawn
Holding up can follow the trail of the number of following users, and the user once performs search A and then performs search B and subsequently returned for search B
The result set returned performs activity after some search.It will thus be appreciated that the search inquiry (for example, search for B) recommended potentially include or
Person may not include the search inquiry recommended with for generating (or mark) the inquiry of front Reference search (for example, searching for A)
Shared term.
In a kind of example embodiment, session of the algorithm that search and recommended engine are realized to tracking (or record)
Data are analyzed, to determine the inquiry for once carrying out (or similar) identical with the inquiry for currently just carrying out and finally successfully enter
The page that the user of the specified or predetermined activity (for example, being bid in network auction system) of row was checked later
Other search in face.
With reference to Fig. 2 and Biao 1, it is to describe one kind for generating the algorithm 105 that the search for being provided to user is recommended
Method.At operation 110, monitored and configured based on LOOKBACK_PERIOD and SAMPLING_RATE to following the trail of data
To write original session information.At operation 120, by only by two variables (search phrase (term) and forecast classification
(category constraint) describes each unique page view to adjust (trim) session data.Search term and point
Class constraint is such a input variable, and it refers to the group of keyword (or other search phrases or input) and forecast classification
Close.If there is forecast classification, then keyword can be sky, and forecast classification can be root point if keyword is applied
Class.For each searched page (A), search phrase (A1) and forecast classification (A2) are recorded.
At operation 130, for each unique page view (A), it is determined that the page (B) and then checked afterwards, with
And the page (C) and then checked afterwards.At 140, each entry in table (A, then B, subsequently C) is divided into two rows,
Wherein the first row is the unique page view (A and then B) for being followed by next page view, and the second row is to be followed by the next page
The page view (A and then C) of face view.Now, each unique page view should have in the table two rows, unless
The page view is last in user conversation or penultimate page view.
At operation 150, in the case of the forecast classification difference of two pages or the search phrase identical of two pages, institute
Some searched pages are to being removed.This may be stayed in the case where forecast classification not yet changes but search phrase is altered
The list of lower searched page pair.In another embodiment, in the case of the different still search phrase identicals of forecast classification,
Search is not to being removed.In this embodiment, system generates to user and same search phrase is performed in different classifications
Recommend.In addition, in the case where forecast classification and search phrase all change, system can be provided based on previous user behavior
It is expected successfully to recommend.
The number of times that every a pair of searched pages (A and then B) occur with same sequence is counted at operation 160, and
It is each searched page to recording the number (n times occurs in " A and then B ").At operation 170, in the number of times that the sequence is run
(N) less than in the case of MIN_TRAFFIC_COUNT parameters, all of searched page is to counting (n times occurs in " A and then B ") quilt
Remove.
Belong to ripe audience classification in forecast classification, any word in either one in two search phrases is located at the website
Blacklist on, it is all of or in the case that search phrase is comprising complicated search arithmetic symbol (such as minus sign, bracket etc.)
Searched page at operation 180 to alternatively being removed.For each initiating searches page (A), at operation 190, the method
Find a series of " next searched page " (B ' s) and be recording this to the descending of be counted number according to searched page
" next searched page " (B ' s) of row is (for A：B occurs 12 times, and C occurs 9 times, and D occurs 6 times, and E occurs 4 times).Finally, in behaviour
Make at 195, truncate the list of " next searched page " of each initiating searches page (A) so as to less than MAX_REL_
SEARCHES parameters.
Table one
Relevant search is shown in search, list and in retail shop (Cross-Stores) search
As shown in the page view 300 of Fig. 3, relevant search module can be linked as another row under search box and shown
Show on searched page.The current queries that the layout of relevant search is currently running based on user.In order to determine which phase shown
Search is closed, system can analyze search phrase and forecast classification, ignore the every other aspect in addition to attribute.
If some predetermined conditions are present, such as current queries include attribute constraint, or current queries are titles and retouch
Search is stated, then relevant search may be occurred without.In example embodiment, whether relevant search layout occurs depending on current
Whether there is available recommendation in the case of search phrase and forecast classification.In another example embodiment, relevant search work(
Whether can be called and additionally depend on whether other kinds of recommendation is also appeared on searched page.
Solve to recommend conflict
In one embodiment, there are various search amendments (or enhancing) that can be occurred on searched page to push away
Recommend.For example, relevant search, spell check, RIS (search is recommended) and PTS (name of product of search) search amendments is recommended.By phase
Closing the recommendation of searching algorithm formation can mutually conflict with some in the recommendation of other systems/repeat.In order to avoid redundancy, punching
The prominent logic that solves can determine how recommendation coexists, as be explained below.
Solution conflicts with spell check
It is related if spell check is recommended to emerge (emerge rule based on existing) in a kind of example embodiment
Search module can be occurred without.The example spell check being modified to term " chars " with " chairs " as seen from Figure 4 is recommended
Page view 400.
Solution conflicts with PTS's
If one or more PTS (name of product of search) recommend to emerge (emerge rule based on existing), related
Search module can occur, but the recommendation list for occurring can be filtered, to avoid showing the recommendation excessively overlap with PTS.
In a kind of example embodiment, only pushing away can be allowed in related search module when PTS recommends to occur
It is so some recommendations to recommend, wherein the search term in recommending is not the superset of the search term in current queries.When PTS occurs
When, but it is added into inquiry in the case of changing without keyword in other keyword, relevant search is recommended may not be by
Receive.
For example, if when PTS recommends active user's just search " Top Gun " in DVD classification；In this case,
Relevant search recommends " Mission Impossible " to be allowed, but recommends " Top Gun DVD New " not allow.
With reference to Fig. 5, page view 500 is which illustrates, wherein the search to " Deception Point " provides relevant search
" Angels and Demons " and " Digital Fortress ".
Solution conflicts with RIS's
Can be solved by new configuration (RIS_CONFLICT) potential between relevant search and RIS (search is recommended)
Conflict.RIS_CONFLICT can have three kinds of possible values, and the setting can be accurately determined relevant search and how mutual RIS is
Effect, is shown in Table 2.As one skilled in the art will appreciate, " window member (widget) " is typically to be used to distinguish on webpage not on webpage
With the rectangular area (as module) of logical message section.Most webpages are by the various window members for illustrating different types of information
Composition.Sometimes user can customize them and want to see which window member do not wanted to see that with them for what window member.
Table 2
The default setting of the RIS_CONFLICT of all websites can be " RIS and relevant search coexist ".The sample page of Fig. 7
Face view 700 shows that relevant search and RIS recommend how to occur together based on the setting.
It is determined that the recommendation to be shown
When superincumbent Conflict solving rule is lower shown, associated search window part can be under search box
Show.Recommending can be always as single link horizontally toward with single file appearance.Five site specific usage configurations can be controlled
The display properties of associated search window part, as shown in table 3.
Table 3
Closing relevant search using MAX_REL_SEARCHES can be done as follows.If MAX_REL_SEARCHES is set
For 0, then relevant search may be not present in any page (for example, search, list, across retail shop search, the dynamic log page
(DLP), individualized webpage) on.Otherwise, MAX_REL_SEARCHES only to searched page (for example, search, list, search across retail shop
Rope) have an impact.In other words, MAX_REL_SEARCHES only affects search, list and across retail shop search, but fills when being set to 0
When generic features closing switch.
It is assumed that MAX_REL_SEARCHES is not set at zero, then recommend to be selected as in search, list and in retail shop
Occur as follows.If MIN_ATTEMPTED_RS_RECOS and MIN_ATTEMPTED_SA_RECOS sums are more than MAX_REL_
SEARCHES, then MIN_ATTEMPTED_RS_RECOS and MIN_ATTEMPTED_SA_RECOS can be ignored.(situation does not have
It is meaningful, and be parameter by mistake arrange instruction.)
With reference to Fig. 6, the flow chart 600 for managing recommendation to display is which depict.At operation 610, from phase
Close search system for current search phrase-classification to it is all available recommend be retrieved.At operation 620, recommend by group
It is made into its corresponding type.If all keywords of current search phrase can be found in the search phrase recommended, push away
It is " search refinement " to recommend type, and otherwise, type of recommendation is " search is substituted ".
At operation 630, M search substituted type of head is recommended selected (based on frequency counting, such as in relevant search algorithm portion
Described in point), wherein M is the setting of MIN_ATTEMPTED_SA_RECOS.A N number of search refinement type is also selected to recommend (to be based on
Frequency counting, as described in relevant search algorithm part), wherein N is the setting of MIN_ATTEMPTED_RS_RECOS.Choose
Recommendation operation 640 at sort by type, then by frequency counting from high to Low sequence, wherein by RECO_TYPE_
The setting of ORDER is determining preferred type.
At operation 650, any type of sub-optimal recommendation is selected, until the sum of the recommendation chosen is equal to MAX_REL_
Till SEARCHES.After recommending to be chosen, these recommendations can be sorted.Clooating sequence in the set can be based only upon
Frequency counting.Finally, at operation 660, start to truncate the complete list of the recommendation chosen from recommendation recently, it is total until what is recommended
Till number of characters (considering that each can be made to recommend four separate characters) is not more than MAX_CHAR parameters.
If the relevant search not shown is recommended after these rules are performed, associated search window part can be with
Occur without completely, and its in yet another case by the space for occupying will compress.Recommendation in associated search window part is worked as
Always can preferentially be ranked up according to most relative when being presented.
Display properties
In a kind of example embodiment, can show that relevant search is recommended based on following rule.Part labels can
To be " relevant search " or part labels can be " popular keyword ".(except last is pushed away after each recommended links
Recommend outside link), comma (not by hyperlink) can be illustrated.In addition, character can separate each recommendation, in recommended links
Being used for any word of current queries can occur with runic, and not be used for any of current queries in recommended links
Word can not occur with runic.For double byte website, font size can be standard, and for every other website,
Font size can be less.
Fig. 8 A are illustrated for the example page view 800 in English area-other west areas.Similarly, Fig. 8 B diagrams
The example page view 810 in non-english area.
Navigation
Clicking on relevant search is recommended can direct the user to another searched page, wherein previous search phrase
It is changed to the still every other search parameter/filter/sequence of new search phrase to be kept.For example, if pushed away
Recommend clicked not yet application class constraint before, then will not application class constraint after recommendation is clicked.If recommended
Application class constraint before clicked, then also will be using identical forecast classification after recommendation is clicked.Recommending quilt
After click, any additional filter (search option, mark selection etc.) also will be kept.After recommendation is clicked, use
The search that family has been applied will continue to be employed.If user was once in across retail shop search, it will be still in searching for across retail shop.
If it is once searched in core, it will be searched for still in core.
SsPageName is followed the trail of
Multiple ssPageName can be added into the ending of the finger URL (anchor) of each recommended links.Additional
The form of ssPageName can Shi ＆ssPageName=RELS:SA<X>:RS<Y>:<TYPE>, wherein<X>It can be integer
Value (0 to n).The number that the search substituted type that it can be shown in associated search window part is recommended is counted.<Y>Can be
Integer value (0 to n).It can be shown in the number counting that the search refinement type in associated search window part is recommended.<TYPE
>Can be one of RS or SA the two values.The value can be RS if (link) type of recommendation is search refinement, if
(link) type of recommendation is that then the value can be SA to search replacement.
The example of ssPageName
Example 1：If two search are substituted to recommend to recommend to be illustrated with three search refinements, each search is substituted to be recommended
SsPageName can Shi ＆ssPageName=RELS:SA2:RS3:SA, the ssPageName that each search refinement type is recommended
Can Shi ＆ssPageName=RELS:SA2:RS3:RS.
Example 2：If zero search is substituted to recommend to recommend to be illustrated with four search refinements, each search refinement type is pushed away
The ssPageName for recommending can Shi ＆ssPageName=RELS:SA0:RS4:RS.
Vertebra recommends tissue
If the available recommendation of the current search not used for such as DLP (the dynamic log page), correlation is searched
Rope module can be occurred without completely.DLP is the centre that multiple navigation options are provided a user with based on the initial search query of user
Searched page, therefore for DLP of the present invention is classified as searched page.If for there is available pushing away in current DLP search
Recommend, then relevant search module can occur.These recommendations are shown in order to determine how, can be primarily based on as above identical
These recommendations are organized into search refinement and are searched for and substituted by logic.
To show but not search for replacement and to show if searching for refinement, then search for refinement and can take up whole mould
Block.Search refinement can be illustrated with three row, be first according to go sequence, then according to row sequence, as shown below：
The link 3 of the link of link 12
The link 6 of the link of link 45
The link of link 78
When only search refinement occurs, most 15 search refinements can be illustrated.
To show but not search for refinement and to show if searching for replacement, then search for replacement and can take up whole mould
Block.Search replacement can be illustrated with three row, be first according to go sequence, then according to row sequence, as shown below：
The link 3 of the link of link 12
The link 6 of the link of link 45
The link of link 78
When only search replacement occurs, most 15 search replacements can be illustrated.
If for DLP has two kinds of recommendation (search refinement and search are substituted) available, figure water can be passed through
These recommendations are divided into two parts by flat separator.For each section, relevant search can be illustrated with three row, be first according to the row of going
Sequence, then according to row sequence, as shown below.
The link 3 of the link of link 12
The link 6 of the link of link 45
The link of link 78
The order of two subdivisions in DLP can set depending on RECO_PREFERENCE_TYPE parameters described above
Put.If RECO_PREFERENCE_TYPE is search refinement, search refinement can occur in search and instead go up.If
RECO_PREFERENCE_TYPE is that search is substituted, then searching for replacement can occur on search refinement.
No matter whether window member is separated, and the label of applicable type of recommendation can be occurred on these recommendations.
The label of search refinement can be " Search Refinement：", it can be " Search to search for the label for substituting
Alternatives：”.
In basic templates, the sum of the recommended links of appearance can be illustrated.(see website text "<X>Head<N>Individual phase
Close search ").N always represents the sum of two kinds of recommendations.The supplemental text is not included in specific project templet.
Fig. 9 A illustrate the design of " basic templates " user interface 900, wherein only search refinement occurs.Fig. 9 B are illustrated
The design of " basic templates " user interface 910, wherein there is two kinds of type of recommendation to occur.Similarly, Figure 10 illustrates " detailed programs
The interface 1000 of template ", wherein only search refinement occurs.Figure 11 illustrates the interface of " detailed programs template "
1100, wherein there is two kinds of type of recommendation to occur.
Relevant search is shown on individualized webpage
Searched page, original list and DLP may need the New function for writing information into cookie.The page of these types
In each cookie can be updated when being checked.Following logic can be used for determining result of page searching, original list
Whether the cookie can be updated with DLP.If search includes property value constraint, cookie is not updated.If search is name
Claim and describe search, then do not update cookie.If search (such as minus sign or is included comprising any complicated search arithmetic symbol
Number), then do not update cookie.Once these inspections are completed, then to the character in search phrase (in the case of double byte language
For byte) calculated and the value is assigned to into X, the number of characters of the ID that classifies is counted and the value is assigned to into Y, and seek X
With Y sums.If X+Y is more than MAX_COOKIE_BYTE_COUNT, cookie is not updated.Otherwise, with the ID of inquiring about and classify
Precise character string is updating cookie.
The information can may be recommended enough in individualized webpage.MAX_COOKIE_BYTE_COUNT is arranged can
Being 40.Which ensure that has not more than 40 bytes occupied in cookie, while allowing to emerge height on individualized webpage
The possibility that quality is recommended is maximized.The recommended value can be examined by suitable channel, to agree.
Relevant search is added into individualized webpage
New module can be created in individualized webpage, to show the recommendation from related search system.Relevant search
The input of module can be the search phrase and forecast classification of the last time search that user is carried out." should search for for the last time " can
With from current sessions or from previous session.It can be obtained in cookie.Output can be the list of recommended links.
Associated search window part can occur according to the new and old order of event, and this is the side that existing module may be sorted aborning
Formula.Associated search window part can be named as " Related Searches ".
If last time search does not include forecast classification：
Associated search window part can include that " your last time is searched for<X>.Here it is some relevant searches."
Subtitle.<X>Represent the search phrase of last time search.<X>Can be clicked on it and can be directed the user to this and searched by hyperlink
The result of page searching of rope phrase.To showing the requirement of recommended links and DLP being wanted in new associated search window part
Ask identical (being described previously).Figure 12 illustrates the individualized webpage scanned in the case of without forecast classification
1200。
If last time search includes forecast classification：
Associated search window part can include " you last time search is (<Y>In)<X>" subtitle.<X>
Represent the search phrase of last time search.If forecast classification is the classification of first rank,<Y>It is first systematic name.If point
Class constraint is L2 classification, then<Y>It is first systematic name of heel " ＞ ", should " ＞ " followed by L2 systematic names.If forecast classification is L3
Or lower classification, then<Y>Be followed by ellipsis (...) first systematic name, the ellipsis heel " ＞ ", should " ＞ " followed by make
The systematic name being employed for constraint.
Entirely go here and there " (<Y>In)<X>" by hyperlink, and can guide execution that there is same search phrase with identical point
The search of class constraint.When there is forecast classification, the subtitle that search refinement and search are substituted also can be changed.Search refinement portion
The subtitle for dividing can be " Search Refinements (in same category) ".Search substitutes the subtitle of part
" Search Alternatives (in same category) ".
Actual search refinement and search substitutes link can be guided to such a search, wherein these keyword quilts
Use, but be restrained to (recommendation is based on) and search for the same classification being restrained to for the last time.Figure 13 is illustrated
The individualized webpage 1300 scanned in the case of having forecast classification.
Whole process is clicked on and whole bid is followed the trail of
Whole process clicks on tracking (click-through tracking) can pass through ssPageName as defined above
To complete.Whole process bid is followed the trail of (bit-through tracking) and whole clicking rate and can be determined.Variable can be added into, with
Indicate that whether searched page is recommended comprising relevant search and whether user once clicked on relevant search and recommend to reach current page.
The value of the new variables for example can be 00,01,10 or 11 based on following rule.If user do not clicked on relevant search with
Then first digit can be 0 to reach current page.First if user once clicked on relevant search to reach current page
Numeral can be 1.Second digit can be 0 if relevant search is recommended and anon-normal is illustrated on current page.If
Relevant search recommends just to be illustrated on current page that second digit can be 1.
Term
Term " list " or " project " are used to provide the example of data, and can refer to any and list, service, offering for sale
(offering) relevant data item, description, identifier, expression or information are either asked.For example, list can be auction or
The offering for sale (for example, the such product of such as commodity and/or service) of person's regular price, advertisement, or to list or service
Request.For herein, word " term " is synonymous with word " phrase ", and alsos attempt to include multiple words.Therefore, " term "
Or " phrase " can be used for referring to that user is input into any one of search field or many when request scans for data system
Individual entry.Term " term-classification to " (or phrase-classification to) can refer to the search term that is associated with particular data classification or
Phrase.
Trading facilities
Figure 14 is the block diagram for illustrating network computer system 1410, and a kind of example embodiment of the present invention can be with
Work in the computer system.Although this is described in the environment of network computer system 1410 in order to illustrate
The illustrative embodiments of invention, the present invention will be many different types of based on computer and network facility sum
According to being applied in processing system.
Network computer system 1410 includes one or more in polytype front-end server, before these
Each includes at least one dynamic link library (DLL) to provide selected function to hold server.System 1410 includes the page
Server 1412, picture servers 1414, listserv 1416, search server 1418 and ISAPI servers 1420, its
Delivering webpage of middle page server 1412 (for example, marking language document), picture servers 1414 are dynamically delivered will be in webpage
The image of middle display, listserv 1416 helps carry out the list browse based on classification, and search server 1418 is processed to being
The searching request of system 1410 and help carry out the browsing data based on keyword, and ISAPI servers 1420 are provided and lead to system
The intelligence interface of 1410 rear ends.System 1410 also includes e-mail server 1412, and the e-mail server 1412 is to base
E-mail communication of automation etc. is provided in the user of the computer system 1410 of network.In one embodiment, one
Individual or multiple management application functions 1424 help to monitor system 1410, maintenance and management.One or more API servers
1426 can provide one group of API function of being used to inquire about and write to network computer system 1410.Can pass through
HTTP transport protocol is calling API.In one embodiment, information is sent and received using the XML data format of standard.
(for example, upload transaction List Table, check that transaction List Table, management are handed over for interacting with network computer system 1410
Easily list etc.) application be designed to use API.This application can be HTML forms, or with C++, Perl,
The cgi script that Pascal or any other programming language are write.Exemplary API is in co-pending U.S. Patent application
Have in 09/999,618 and more fully describe, above-mentioned application is hereby incorporated by reference.
Page server 1412, API server 1426, picture servers 1414, ISAPI servers 1420, search service
Device 1418, e-mail server 1422 and database engine server 1428 can singly or in combination serve as communication and draw
Hold up, to help the communication between such as client computer 1430 and network computer system 1410.Additionally, page server
1412nd, API server 1426, picture servers 1414, ISAPI servers 1420, search server 1418, E-mail service
Device 1422 and database engine server 1428 can singly or in combination serve as transaction engine, to help such as client computer
Transaction between 1430 and network computer system 1410.In addition, page server 1412, API server 1426, figure
Piece server 1414, ISAPI servers 1420, search server 1418, e-mail server 1422 and database engine clothes
Business device 1428 can singly or in combination serve as display engine, to help show list in such as client computer 1430.
Back-end server can include the database engine server for each safeguarding and helping the access to associated databases
1428th, index server 1432 and credit card database server 1434 are searched for.
In one embodiment, by such as browser 1436 (for example, by the Microsoft in Redmond city
The Internet Explorer of distribution) as client-side program accessing network computer system 1410, it is described clear
Device 1436 of looking at is performed in client computer 1430 and accesses network computer via network (for example, internet 1438)
System 1410.Other examples for the network that can be used by a client to access network computer system 1410 include wide area network
(WAN), LAN (LAN), wireless network (such as Cellular Networks), public switch telephone network (PSTN) etc..In client computer 1430
The client-side program of execution can be communicating via API server 1426 with network computer system 1410.
Database structure
Figure 15 diagrams are safeguarded by database engine server 1428 and accessed via database engine server 1428
The database diagram of exemplary database 1540, the database engine server 1428 is realized at least in part and supported based on net
The computer system 1410 of network.In one embodiment, database engine server 1428 can safeguard two databases, institute
The first database safeguarded is used to list institute's information not to be covered in (or offer) virtual " retail shop ", and the second database is used to arrange
Go out the information that (or offer) provides via virtual " retail shop " that network computer system 1410 is supported.
In one embodiment, database 1540 can be implemented as relational database and including it is multiple with entry or
The table of record, these entries or record are connected by index with keyword.In another embodiment, database 1540 can
The object set being implemented as in object-oriented database.
Database 1540 includes user's table 1542, and user's table 1542 is comprising in network computer system 1410
The record of each user.User can take on seller, buyer or both when using network computer system 1410.
Database 40 also includes that the list table (listing table) 44 of user's table 42 can be linked to.List table 44 can include selling
Family's list table 46 and bid list table 48.User record in user's table 42 can be linked to or via being based on
The computer system 10 of network list or offering for sale multiple lists.In one embodiment, link indicates user
It is seller or bidder (buyer in other words) relative to the list for having record in list table 44.
One or more parts presented in the form of the classification that database 1540 also includes provided in classification chart 1550.
Each record in classification chart 1550 can describe corresponding classification.In one embodiment, the list that system 10 is provided
By according to classification arrangement.These classification can be used for positioning in specific classification by the user of network computer system 1410
List.Therefore, classification provides a kind of mechanism for being positioned to browsable list.As supplementing or substituting, search
Rope server 1420 can provide a kind of alphanumeric search mechanism, so that user searches for spy using search term or phrase
Determine list.In one embodiment, classification chart 1550 describes the data structure of multiple classifications and including multiple classification
Record, each these book of final entry describe the context of the specific classification in multiple classification structures.For example, classification chart 1550
List records in the list table 1544 various true classification to be linked to actual classification in other words can be described.
Database 1540 also includes one or more attribute lists 1552.Each record description and list in attribute list 1552
Associated respective attributes.In one embodiment, attribute list 1552 describes multiple hierarchical nature data structures and including many
Individual attribute record, each these attribute record describe the context of the particular community in multiple hierarchical nature structures.For example, attribute
Table 1552 can describe the various real properties to be linked to of the list records in list table 1544 actual attribute in other words.Separately
Outward, attribute list 1552 can be with the real property to be linked to of the classification in interpretive classification table 1550 actual attribute in other words.
Database 1540 can also include being filled with the annotation table 1554 of annotation record, and the annotation record can be linked
To one or more list records in list table 1544 and/or one or more use being linked in user's table 1542
Family records.Each annotation record in annotation table 1554 can include and be supplied to via network computer system 1410
The relevant comment of the list of the user of the system, description, history or other information etc..Database 1540 can also include by
The targeted sites table 1556 recorded with targeted sites is filled, the targeted sites record can be linked in list table 1544
Individual or multiple list records and/or one or more user records being linked in user's table 1542.
Multiple other sample tables can also be linked to user's table 1542, and these tables are the passing alias table 1558 of user, feedback
Table 1560, feedback details table 1562, bid table 1564, credit 1566 and account balance table 1568.In one embodiment,
Database 1540 also includes batch table 1570, batch list table 1572, and list waits table 1574.
In one embodiment, the search of the user of the life of system 1410 paired systems 1410 is recommended.Search recommendation can be with
The passing user mutual carried out based on specific user and system 1410, and network computer system 1410 (or appoint
The system that what he is associated with network computer system 1410) used in search term.
With reference to Figure 16, label 1680 is usually indicated based on passing bid (and/or purchase) Historical form with user
The passing user mutual that presents and search term are generating the example logic of recommendation query.As shown in block 1682, in data bins
Passing bid (and/or purchase) data of participating user are collected at storehouse.Additionally, collect at block 1684 popular search term or
Person's phrase, these popular search terms or phrase are looked into together with passing bid (and/or purchase) data for generating recommendation
Ask (see block 1686).Therefore, data warehouse can be identified and be stored in what is be associated with network computer system 1410
One or more subscribe search term (popular search term) the most used between number website (such as website), also identify
The data being uniquely associated with each user.As shown in block 1688, popular search term then can be by periodically (for example
Pass to production facility daily), wherein production facility then can by popular search data projection to the current list stock (see
Block 1690).In one embodiment, carried out by each classification in each category level using each popular search term
Search.The prevalence that the list (such as 50 lists) of all at least predetermined numbers with each specific classification matches is searched
Suo Shuyu can be stored together with the sum of the list of the use popular search term in the specific classification.Therefore,
Each classification can be allocated some (for example, from 0 to predetermined number) popular search terms or phrase and the search phrase
Popularity measurement result in the classification.Therefore, system 10 is allowed based on popular search (being based on the interaction of all users)
Search is performed by the current list with the unique historical interaction of user.
Figure 17 shows the diagram of the machine presented with the exemplary forms of computer system 1700, in computer system 1700
In can perform one group of command sequence for making machine perform any one of method discussed herein method.Substituting
In embodiment, machine can include network router, the network switch, bridge, personal digital assistant (PDA), honeycomb fashion electricity
Words, network equipment, Set Top Box (STB), or any machine for being able to carry out following command sequences, the command sequence is specified should
Machine action to be taken.
Computer system 1700 includes processor 1702, main storage 1704 and the static state communicated via bus 1708
Memory 1706.Computer system 1700 can also include video display unit 1710 (for example, liquid crystal display (LCD) or
Cathode-ray tube (CRT)).The also bag Alphanumeric Entry Device 1712 of computer system 1700 (for example, keyboard), cursor control set
Standby 1714 (for example, mouses), disk drive unit 1716, signal generation equipment 1718 (for example, loudspeaker) and network interface
Equipment 1700, the Network Interface Unit 1700 is used to for computer system to be connected to network 1722 by interface.
Disk drive unit 1716 includes machine readable media 1724, is stored with machine readable media 1724 and realizes this
In any one of methods described either one group of instruction of whole or software 1726.Software 1726 be also depicted as it is complete or
Person is resided at least partially within main storage 1704 and/or processor 1702.Software 1726 can be setting via network interface
Standby 1720 are transmitted or receive.For herein, term " machine readable media " should be read to include and any can store
Or coding is performed for machine and makes machine perform the medium of any one of the method for the present invention command sequence of method.Art
Language " machine readable media " should correspondingly be understood to include but be not limited to solid-state memory, optics and magnetic plate, Yi Jizai
Ripple signal.In addition, though software is shown residing within individual equipment in fig. 17, but it will be appreciated that software 1726 can quilt
It is distributed between the multiple machines or storage medium that may include machine readable media.Method described herein can be used to improve use
The browse efficiency at family, uses the more efficient of computing resource so as to facilitate.
Although describing the present invention by reference to specific example embodiment, but it is clear that can be without departing from the present invention's
Various modifications and changes are carried out to these embodiments in the case of broader spirit and scope.Therefore, specification and drawings should be with
Descriptive sense and non-limiting sense are treating.
Claims (19)
1. a kind of method, including：
Current queries are received from active user；
Determine once to perform using one or more processors and inquire about, performing described first with the current queries identical first
Perform after inquiry the second inquiry and in response to described second inquiry present one or more Search Results perform to
The counting of the number of the previous user of activity after a few search；And
Count in response to determined by and exceed threshold value, the recommendation that the described second inquiry is automatically designated as first inquiry is looked into
Ask, second inquiry will be presented to the active user.
2. the method for claim 1, also includes：
Described second inquiry is presented to into the active user as recommendation query, the presentation includes causing the second inquiry quilt
It is illustrated as what is submitted to from previous user one or more described.
3. the method for claim 1, wherein second inquiry includes multiple queries, and this multiple queries includes first
Group polling and the second group polling, and described specifying automatically include：
Each inquiry in response to determining first group polling includes all keywords in first inquiry, will be described
First group polling is set at least one refined queries of first inquiry；And
Each inquiry in response to determining second group polling does not include all keywords in the described first inquiry, will
Second group polling is set at least one replacement query of first inquiry.
4. method as claimed in claim 3, also includes：
At least one refined queries and at least one replacement query are presented to into the active user simultaneously, the presentation
It is shown as separate inquiry group including at least one refined queries and at least one replacement query is caused.
5. method as claimed in claim 3, wherein, each inquiry in second group polling does not include any with described the
The common keyword of one inquiry.
6. the method for claim 1, wherein the determination includes：
The 3rd inquiry is performed after first inquiry is performed and before second inquiry is performed in response to determining, no
Count determined by increase.
7. the method for claim 1, also includes：
Check second inquiry whether including search arithmetic symbol；And
In response to determining that second inquiry includes that search arithmetic is accorded with, the described second inquiry recommendation query is not appointed as into.
8. the method for claim 1, also includes：
Check whether forecast classification is identical for both the described first inquiry and the described second inquiry；And
In response to determining for both the described first inquiry and the described second inquiry forecast classifications are differed, described second is not looked into
Inquiry is appointed as recommendation query.
9. the method for claim 1, wherein second inquiry includes multiple queries, and described specifying automatically is wrapped
Include：
Count to determine the ranking of the plurality of inquiry based on determined by the respective queries in the plurality of inquiry.
10. a kind of system, including：
Memory, for storing search history information；And
One or more processors, it is communicatively coupled in the memory, and the one or more processors are used to hold
Row inquiry recommended engine, the inquiry recommended engine is configured to：
Current queries are received from active user；
It is determined that once performed and inquiring about, performing the second inquiry after first inquiry is performed with the current queries identical first
And one or more Search Results to presenting in response to the described second inquiry perform the elder generation of activity after at least one search
The counting of the number of front user；And
Count in response to determined by and exceed threshold value, the recommendation that the described second inquiry is automatically designated as first inquiry is looked into
Ask, second inquiry will be presented to the active user.
11. systems as claimed in claim 10, wherein, the inquiry recommended engine is configured to：
Described second inquiry is presented to into the active user as recommendation query, the presentation includes causing the second inquiry quilt
It is illustrated as what is submitted to from previous user one or more described.
12. systems as claimed in claim 10, wherein, second inquiry includes multiple queries, and this multiple queries includes the
One group polling and the second group polling, and it is described inquiry recommended engine be configured to：
Each inquiry in response to determining first group polling includes all keywords in first inquiry, will be described
First group polling is set at least one refined queries of first inquiry；And
Each inquiry in response to determining second group polling does not include all keywords in the described first inquiry, will
Second group polling is set at least one replacement query of first inquiry.
13. systems as claimed in claim 12, wherein, the inquiry recommended engine is configured to：
At least one refined queries and at least one replacement query are presented to into the active user simultaneously, the presentation
It is shown as separate inquiry group including at least one refined queries and at least one replacement query is caused.
14. systems as claimed in claim 12, wherein, in second group polling each inquiry include it is any with it is described
The common keyword of first inquiry.
15. systems as claimed in claim 10, wherein, the inquiry recommended engine is configured to：
The 3rd inquiry is performed after first inquiry is performed and before second inquiry is performed in response to determining, no
Described second inquiry is appointed as into recommendation query.
16. systems as claimed in claim 10, wherein, the inquiry recommended engine is configured to：
Check whether second inquiry includes syntax error；And
In response to determining that second inquiry includes syntax error, the described second inquiry recommendation query is not appointed as into.
17. systems as claimed in claim 10, wherein, first inquiry includes name of product, and the inquiry is recommended
Engine is configured to：
Check whether second inquiry is Chong Die with the name of product；And
It is Chong Die with the name of product in response to determining second inquiry, the described second inquiry is not appointed as into recommendation query.
18. systems as claimed in claim 10, wherein, activity after at least one search is included in the following at least
One：One or more products or service that purchase is identified by one or more of Search Results, to one or many
Bid in the auction of individual product or service, or sent to one or more of products or the further information of service
Inquiry.
A kind of 19. equipment, including：
For receiving the device of current queries from active user；
Inquire about, perform second after first inquiry is performed with the current queries identical first for determining once to perform
Inquire about and one or more Search Results to presenting in response to the described second inquiry perform activity after at least one search
Previous user number counting device；And
Count in response to determined by and exceed threshold value, for the described second inquiry to be automatically designated as the recommendation of first inquiry
The device of inquiry, second inquiry will be presented to the active user.
Applications Claiming Priority (5)
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| US60/692,125 | 2005-06-20 | ||
| US11/323,486 US8200687B2 (en) | 2005-06-20 | 2005-12-30 | System to generate related search queries |
| US11/323,486 | 2005-12-30 | ||
| CN2006800222689A CN101203856B (en) | 2005-06-20 | 2006-06-20 | System to generate related search queries |
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| CN103150352B true CN103150352B (en) | 2017-05-03 |
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| CN2006800222689A Active CN101203856B (en) | 2005-06-20 | 2006-06-20 | System to generate related search queries |
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| AU (2) | AU2006262446B2 (en) |
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| CN101203856A (en) | 2008-06-18 |
| AU2010202828A1 (en) | 2010-07-22 |
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| EP1899867A2 (en) | 2008-03-19 |
| US9183309B2 (en) | 2015-11-10 |
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| US20160140165A1 (en) | 2016-05-19 |
| JP2008544377A (en) | 2008-12-04 |
| JP4813552B2 (en) | 2011-11-09 |
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